Utilizing bluetooth and adaptive signal control data for real-time safety analysis on urban arterials

Abstract

Real-time safety analysis has been widely adopted to reveal the relationship between real-time traffic characteristics and crash occurrence, and these results could be applied to improve active traffic management systems and enhance safety performance. Most of the previous studies have been applied to freeways and seldom to arterials. This study attempts to examine the relationships between crash occurrence and real-time traffic and signal timing characteristics based on four urban arterials in Central Florida. Bayesian conditional logistic models (BCL) were developed by incorporating the Bluetooth, adaptive signal control, and weather data, which were extracted for a period of 20 min (four 5-minute intervals) before the time of crash occurrence. Model comparison results indicated that the model based on 5–10 min interval dataset performed the best. It revealed that the average speed, upstream left-turn volume, downstream green ratio, and rainy indicator were found to have significant effects on crash occurrence. Furthermore, Bayesian random parameters conditional logistic model (BRPCL) outperformed Bayesian random parameters logistic (BRPL) and Bayesian conditional logistic models (BCL) in terms of the area under the receiver operating characteristics curve (AUC) and Deviance Information Criterion (DIC) values. These results are important in real-time safety applications in the context of Integrated Active Traffic Management (IATM).